code: def query_with_distances(self, v, n):
      v = np.ascontiguousarray(v, dtype=self.index.dtype)
      if self._metric == "angular" or self._metric == "dot":
          dists = -np.dot(self.index, v)
      elif self._metric == "euclidean":
          dists = self.lengths - 2 * np.dot(self.index, v)
      else:
          assert False, "invalid metric"
      indices = np.argpartition(dists, n)[:n]
  
      def fix(index):
          ep = self.index[index]
          ev = v
          return (index, pd[self._metric]["distance"](ep, ev))
  
      return map(fix, indices)
  
file_path: n2/benchmarks/data/create_youtube_dataset.py
imports: import sys
  import h5py
  import numpy
  from sklearn.model_selection import train_test_split
  from metrics import metrics
label_desc: Use image node_id 3 for calling the BruteForceBLAS obj's underlying member method code with example usage: obj.query_with_distances(v, n) and returns: map, index,  pd